parallel-data-enrichment 是做什麼的？
批量數據豐富化。為公司、人員或產品清單添加從網絡檢索的欄位（CEO姓名、融資資訊、聯繫方式等）。適用於CSV檔案或內嵌數據的豐富化。支援多輪對話：傳遞 --previous-interaction-id 以承接先前的研究任務。
批量數據豐富化。為公司、人員或產品清單添加從網絡檢索的欄位（CEO姓名、融資資訊、聯繫方式等）。適用於CSV檔案或內嵌數據的豐富化。支援多輪對話：傳遞 --previous-interaction-id 以承接先前的研究任務。
這項技能是 `parallel-web/parallel-data-enrichment`，用於高效地批量豐富化數據。它能從網絡來源自動擷取並補充特定欄位，例如CEO姓名、融資輪次、聯繫方式等，適用於公司、人員或產品清單。此功能特別適合處理CSV檔案或直接在工具中處理內嵌數據，無需手動查詢每個項目。該技能支援多輪對話，允許使用者傳遞 `--previous-interaction-id` 參數，從先前的數據研究任務中攜帶上下文，實現連續且迭代的數據豐富化流程。這對於需要逐步優化或擴充數據集的開發者及AI代理使用者特別有用，能大幅提升數據處理效率與準確性。
npx skills add https://github.com/parallel-web/parallel-agent-skills/tree/main/skills/parallel-data-enrichmentInstalled? Explore more 研究與資料分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
批量數據豐富化。為公司、人員或產品清單添加從網絡檢索的欄位（CEO姓名、融資資訊、聯繫方式等）。適用於CSV檔案或內嵌數據的豐富化。支援多輪對話：傳遞 --previous-interaction-id 以承接先前的研究任務。
This repo contains 8 individual skills — each has its own dedicated page.
ONLY use when user explicitly says 'deep research', 'exhaustive', 'comprehensive report', or 'thorough investigation'. Slower and more expensive than parallel-web-search. For normal research/lookup requests, use parallel-web-search instead. Supports multi-turn: pass --previous-interaction-id from a prior research or enrichment to continue with context.
Discover entities (companies, people, products, etc.) matching a natural-language description. Use when the user asks to 'find all X' or 'list every Y that…' — e.g., 'Find AI startups that raised Series A in 2026', 'List roofing companies in Charlotte NC', 'Show me YC W24 dev tools companies'. Different from web-search (which returns webpages) and deep-research (which returns a narrative report). Use this when the user wants a structured list of entities.
Continuously track the web for changes on a recurring cadence. Use when the user asks to 'monitor', 'track changes to', 'watch', or 'alert me when' something on the web changes — e.g., 'Track price changes for iPhone 16', 'Alert me when Tesla files a new 8-K', 'Monitor competitor pricing pages weekly'. Also use to list, inspect, update, or delete existing monitors.
URL content extraction. Use for fetching any URL - webpages, articles, PDFs, JavaScript-heavy sites. Token-efficient: runs in forked context. Prefer over built-in WebFetch.
DEFAULT for all research and web queries. Use for any lookup, research, investigation, or question needing current info. Fast and cost-effective. Only use parallel-deep-research if user explicitly requests 'deep' or 'exhaustive' research.
Get completed research task result by run ID
Set up the Parallel plugin (install CLI)
Check running research task status by run ID
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.